Results (PhD Chapter 2)
Section 2/2
Results (PhD Chapter 2)
This series of files compile all analyses done during Chapter 2:
- Section 1 presents indices of influence calculations.
- Section 2 presents HMSC and regressions results.
All analyses have been done with R 3.6.0.
Click on the table of contents in the left margin to assess a specific analysis
Click on a figure to zoom it
To assess Section 1, click here.
To go back to the summary page, click here.
Human activities considered for the analyses:
- city influence: CityInf
- industries influence: InduInf
- dredging collecting zones: DredColl
- dredging dumping zones: DredDump
- commercial ships mooring site: MoorSite
- rainwater sewers: RainSew
- wastewater sewers: WastSew
- city wharves: CityWha
- industries wharves: InduWha
- fisheries (gear used):
- traps: FishTrap
- bottom-trawling: FishTraw
- longline: FishLine
- nets: FishNet
- dredge: FishDred
Data is also available for the number of captured individuals for dogwhelk (Buccinum sp.), common crab (Cancer irroratus), snowcrab (Chinoecetes opilio), nordic shrimp (Pandalus borealis), arctic surfclam (Mactromeris polynyma) and american lobster (Homarus americanus) fisheries.
1. Exploration of relationships between parameters
This section explores relationships between each pair of parameters or AH distances.
Depth
OM
Gravel
Sand
Silt
Clay
CityInf
InduInf
DredColl
DredDump
MoorSite
RainSew
WastSew
CityWha
InduWha
2. Hierarchical Modelling of Species Communities
We will use the probabilities and indices of influences calculated in Section 1 here. The aim is to obtain predictive models for the benthic communities, based on the abiotic parameters and the human activities.
HMSC models have been developped in a dedicated script, and the R workspace has been imported here.
First, we initiate the HMSC model with the chosen data, priors and parameters.
Here are the diagnostics to evaluate the model’s quality.
Trace plots
Explanatory power
Confidence intervals
Credible intervals
Variance partitioning
Association networks
Finally, we can predict the values of our parameters within BSI.